Establishing communication and power sharing links between components of a distributed energy system

ABSTRACT

Disclosed herein is a method and system for sharing power or energy across various power supply and control modules. More specifically, disclosed herein are systems and methods for distributing energy. As explained herein, the method discloses receiving, at a microgrid, data from a plurality of data sources. The data is then analyzed to forecast power needs associated with the microgrid. Using the data, the microgrid may determine whether and when to share power with the requesting module.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a nonprovisional patent application of and claimsthe benefit of priority to U.S. Provisional Patent Application No.61/975,829, filed Apr. 6, 2014 and titled Fractalgrids andFractalhives,” the disclosure of which is hereby incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure is directed to providing and sharing powerbetween various components of a distributed energy system. Morespecifically, embodiments of the present disclosure are directed tomicrogrids in a distributed energy system that may share and receivepower and information with other components or microgrids within thedistributed energy system based on current and/or forecasted powerneeds.

BACKGROUND

Conventional power stations supply energy and power to a number ofhomes, businesses, cities, municipalities, counties and so on.Typically, these power stations are in a central location and, as aresult, must transmit power, in the form of electricity, over longdistances. As electricity travels over such distances, some of the poweris lost. Accordingly, what is needed is a scalable distributed energysystem that may be located close to one or more loads that require thepower provided by the distributed energy system.

It is with respect to these and other general considerations thatembodiments have been made. Although relatively specific problems havebeen discussed, it should be understood that the embodiments should notbe limited to solving the specific problems identified in thebackground.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription section. This summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

Embodiments disclosed herein are directed to a system and method forproviding data and energy links between various nodes of a distributedenergy system. More specifically, embodiments of the present disclosureare directed to components of a distributed energy system that providepower or serve as a load to other components of the distributed energysystem based on a defined set of rules.

For example and as will be described below, the embodiments describedherein are directed to receiving, at a microgrid, data from a pluralityof data sources. The received data is analyzed to determine and/orforecast power needs of various components associated with or otherwisedependent on the microgrid. Using the data, the microgrid may determinewhether and when to share power with the requesting component. In someembodiments, the requesting component may be a child microgrid, a parentmicrogrid or other component or module that requests or otherwiserequires power. Such examples include energy storage modules, energygeneration modules, energy sinks or loads, sensors and so on.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure may be more readily described byreference to the accompanying drawings in which like numbers refer tolike items and in which:

FIG. 1 illustrates an exemplary distributed energy system according toone or more embodiments of the present disclosure;

FIG. 2 illustrates an exemplary microgrid according to one or moreembodiments of the present disclosure;

FIG. 3 illustrates an exemplary method for distributing energy accordingto one or more embodiments of the present disclosure; and

FIG. 4 is a block diagram illustrating physical components of anexemplary computing device that may be used with one or more embodimentsof the present disclosure.

DETAILED DESCRIPTION

Various embodiments are described more fully below with reference to theaccompanying drawings, which form a part hereof, and which show specificembodiments for practicing the embodiments described herein. However,various embodiments may be implemented in many different forms andshould not be construed as limited to the embodiments set forth herein;rather, these embodiments are provided so that this disclosure will bethorough and complete, and will fully convey the scope of the inventionto those skilled in the art. Embodiments may be practiced as methods,systems or devices. Accordingly, embodiments may take the form of ahardware implementation, an entirely software implementation or animplementation combining software and hardware aspects.

In mathematics, a fractal is a mathematical set that exhibits arepeating or closely-repeating pattern. The pattern also displays thesame architecture at every scale. This concept of fractals is extendedherein to define a distributed energy system known as a FRACTALGRID. AFRACTALGRID is a type of distributed and networked microgridarchitecture in which a larger microgrid is comprised of smallermicrogrid nodes or fractal units. Each microgrid or fractal unit hasself-similar attributes that enable interoperability with othermicrogrids and also enable various microgrids to be integrated into asimilar, often larger architecture with ease.

Because each microgrid in a system may be similar or otherwise havesimilar structure and/or components, information may be easily sharedbetween microgrids. For example, cybersecurity information may be sharedamong the microgrids at all levels throughout similar patterns,resulting in inherent similar behaviours.

A FRACTALGRID may provide a reliable source of electricity generation asthe FRACTALGRID is configured to integrate locally-available renewableresources. More specifically, the FRACTALGRID may utilize localizedrenewable resources for electricity generation and can also integraterenewable energy with the local utility grid via semi-autonomouscontrols that may enhance the ability of a consumer to maintain energydelivery during outage events or shortfalls in energy availability. Thiscapability, combined with a cybersecure information-sharing platform,greatly enhances energy security and reliability.

In some implementations a microgrid may be more resilient to adversegrid conditions such as voltage and frequency anomalies and poweroutages than conventional power grids or subsystems of conventionalpower grids. For example, when such adverse grid conditions or eventsare detected, the microgrid is capable of disconnecting from the utilitygrid and autonomously sustaining power for minutes to weeks dependingupon local distributed resource generation, energy storage, andintelligent energy management strategies. The microgrid can also detectthe recovery of normal grid behavior and reconnect for normal paralleloperation after syncing reference voltage and frequency.

In addition to the above, a microgrid may have many operational modesand operational sub-modes. Some of these include, but are not limited topeak-shaving, load-shifting, and load-leveling. These operational modesmay allow consumers or other users to offset energy and powerconsumption from the utility through locally produced renewable energyand energy storage.

In light of the above, embodiments described herein are directed todistributed energy systems and more particularly to enabling one or morecomponents of a distributed energy system to communicate with othercomponents of the distributed energy system. In addition to enabling thevarious components of the distributed energy system to communicate withone another, embodiments described herein also enable the variouscomponents of the distributed energy system to share power with othercomponents of the system. As will be explained below, the sharing ofpower may be based on a defined set of rules or priorities that iscommonly shared among the different components of the distributed energysystem.

The distributed energy system described herein may include one or moremicrogrids. As described above, a microgrid is an electrical system thatis configured to locally generate, store and distribute power. Examplesof microgrids include, but are not limited to renewable energy sourcessuch as solar power, wind power, geothermal power, hydro power,electrical transfer switches, energy generation modules, energy storagemodules, loads, local sensors and so on. Microgrids can functionindependently and may also be connected to large electrical grids ormacrogrids. That is, a microgrid can not only generate its own power,but it can both receive power from a macrogrid and provide power to amacrogrid. In addition to the above, a microgrid can receive requestsfor power from other components in the distributed energy system, anddetermine, based on historical, current and/or forecasted power needs,whether and when to share power with the requesting components.

FIG. 1 illustrates an exemplary distributed energy system 100 accordingto one or more embodiments of the present disclosure. As shown in FIG.1, the distributed energy system 100 may include a number of differentcomponents or modules that are communicatively and/or electricallycoupled together. This coupling may enable each component within thedistributed energy system 100 to share data and/or power with othercomponents of the distributed energy system 100.

More specifically, and as shown in FIG. 1, the distributed energy system100 includes a macrogrid 110. As briefly explained above, the macrogrid110 may be a centralized power grid such as a power station that isconfigured to generate or otherwise provide power to various locations,components, loads, modules and the like. Exemplary power stationsinclude nuclear power plants, coal-fired power plants, hydroelectricdams, large scale solar panels/plants and so on.

In some embodiments, the macrogrid 110 may be electrically and/orcommunicatively coupled to one or more child microgrids such as shown inFIG. 1. The link between the microgrids and the macrogrid 110 enablesthe macrogrid 110, and each of the microgrids in the distributed energysystem 100, to share data and power with each member in its family tree.That is, power may be transmitted from one parent microgrid in thedistributed energy system 100 to one or more child microgrids and so onLikewise, power may be provided from a child microgrid to a parentmicrogrid.

For example and as shown in FIG. 1, macrogrid 110 is communicativelyand/or electrically coupled to microgrid 1 120, microgrid 2 130 andmicrogrid 3 140. Such a coupling enables the macrogrid 110 to sharepower and data with, and receive power and data from, each of microgrid1 120, microgrid 2 130 and microgrid 3 140 Likewise, the coupling of thecomponents of the distributed energy system 100 enables each microgridto communicate and share power with other microgrids in the distributedenergy system 100.

For example, as shown in FIG. 1, microgrid 1 120 has a plurality ofchild microgrids 122, microgrid 2 130 has a plurality of childmicrogrids 132 and microgrid 3 140 has a plurality of child microgrids142. Each family of microgrids in the distributed energy system 100 maybe configured to communicate and share power with other microgrids inits family. That is, microgrid 1 120 may share power with and receivepower from one or more of its child microgrids 122 Likewise, microgrid 1120 may share power and data with and/or receive data and power frommicrogrid 2 130 and/or child microgrids 132.

Although each microgrid is shown having multiple child microgrids, sucha configuration is not required. That is, a microgrid may have nochildren or many children. Likewise, children may be removed or added asnew microgrids become available or are otherwise discoverable by aparticular microgrid or macrogrid. Regardless of the number of childmicrogrids a given microgrid may have, power and data may be sharedbetween parent and child and child and parent.

In some embodiments, the sharing of data and/or power between siblingmicrogrids may be accomplished through the parent microgrid althoughthis is not required. For example, if microgrid 1 120 requests powerand/or data from microgrid 2 130, the power and/or data would be sent tothe requesting microgrid via macrogrid 110.

The size of the distributed energy system 100 may be scalable. Morespecifically, the size of the system may change in real-time,substantially real-time or based on current and/or forecasted powerneeds. In addition, a single microgrid, or set of microgrids in a givensystem, may be configured to determine whether or not it wants to beabsorbed or otherwise included with another larger (or smaller) system.

For example, a single microgrid may be configured to provide power orotherwise produce power to sustain a particular load (e.g., a house, awater pumping station, a cell phone tower etc.). This single microgridcan be aggregated into larger microgrids to sustain larger areas withlarger power needs. In turn, these aggregated microgrids can be combinedwith otherwise aggregated into other microgrids that may supply power onyet a larger scale.

However, because each microgrid is a self-sustaining unit, the microgridcan determine whether it wants to be part of the larger system. Forexample, the microgrid may determine that based on historical, currentand/or forecasted power usage of a load that it services, there is nobenefit to the microgrid joining the larger system. In otherembodiments, the microgrid may determine, based on received data orhistorical use data, that it will not have sufficient power to serve theload when requested. As such, the microgrid may join or otherwiserequest to join a larger distributed energy system. In still yet otherembodiments, a parent (either a current parent, a former parent or arequesting parent microgrid) may request that the microgrid join and/orleave the distributed energy system 100. As such, the overalldistributed energy system 100 may be scalable. Further, since eachmicrogrid is autonomous the overall system may be maintained with ratherlow complexity.

Additionally, just as the distributed energy system may grow asdescribed above, the system may also shrink when a system, or acomponent of the system, requires less power. That is, the size of thesystem may dynamically change in real-time or substantially real-timedepending on current or forecasted power needs of the distributed energysystem 100. In addition, a microgrid may be manually or automaticallyremoved from the distributed energy system 100 in response to aperceived or actual threat (e.g., physical attack or a cyber-attack),reduced power generation (e.g., over a period of time) or whenmaintenance is to be or is being performed on the microgrid. Once themicrogrid has been serviced or is otherwise operating as expected, themicrogrid may again join the system (either automatically or manually).

FIG. 2 illustrates an exemplary microgrid 200 according to one or moreembodiments of the present disclosure. In some embodiments, themicrogrid 200 may be part of or otherwise associated with one or more ofthe microgrids discussed above with respect to FIG. 1.

As discussed, a microgrid may be an autonomous unit that may beconnected or disconnected (islanded) from a larger grid (such as autility grid, another microgrid, a macrogrid or other such distributedenergy system and so on) at any time. The microgrid 200 may interactwith a parent microgrid and one or more child microgrids using variouscommunication protocols. These communication protocols allow for thesharing of resources, including power and data, and also enables themicrogrid to connect to and disconnect from parent microgrids, and/orchild microgrids in cases of maintenance, emergencies, cyber-attacks,physical attacks and so on such as described above.

As shown in FIG. 2, the microgrid 200 may include a controller 210, adata storage module 220, a data processing module 230 and acommunication module 240. Although shown separately, in someembodiments, some or all of these components may be combined into asingle unit such as, for example, the controller 210.

In some embodiments, the controller 210 is a computing device or othersuch processing device that receives and processes requests and/or data.More specifically, the controller 210 may be a processor, a processingunit, a programmable logic controller, a remote terminal unit and may beconfigured to collect data, code the data into a format that istransmittable between various microgrids and transmit the data betweenthe microgrids.

As also shown, a microgrid may include one or more subsystems such as,for example, subsystem 1 270 and subsystem N 280. These subsystems mayinclude, but are not limited to, electrical transfer switches, energygeneration modules, energy storage modules, loads, local sensors, and soon.

The controller 210 may include software, hardware or other logic andmodules that determines energy resource management including, but notlimited to which subsystems and microgrids need or are requesting powerand whether the microgrid 200 itself should be connected to ordisconnected from a distributed energy system. Because each microgridincludes a controller, each microgrid is an autonomous unit and it canperform or otherwise instruct various modules to act in accordance withthe best interest of the microgrid and/or in the best interest of thesubsystems the microgrid services.

For example, the microgrid 200 may receive data from another microgridor another data source. The data may be received using the communicationmodule 240 and stored in a data storage module 220. In someimplementations, the communication module 240 may include an industrialcontrol system (ICS) or supervisory control and data acquisition (SCADA)system that enables communication and power transfers between microgridsand/or subsystems.

Once the data is received, the data processing module 230 may processthe data to determine current power needs of system serviced by themicrogrid 200 and may also determine how much power the microgrid 200 isproducing. In addition, the data processing module 230 may be able toforecast power requirements of the system and whether the power producedby the microgrid 200 can meet the power demands of the system.

For example, the controller 210 may be configured to determine whetherit has, or has access to, the power that will be required by aparticular system. If the microgrid 200 has access to sufficient power,that power may be provided to the requesting system over a communicationand/or electrical connection. If the microgrid 200 does not havesufficient power, the controller 210 may communicate with othermacrogrids or microgrids (e.g., microgrid 1 250) and request power fromand/or request to join a distributed energy system associated withmicrogrid 1 250.

In some embodiments, the controller 210 may also be configured forenergy management. More specifically, the controller 210 may beconfigured to determine a priority of power flow. That is, thecontroller 210 may be configured to provide power to various requestingmodules in an ordered and systematic way. Table 1 below shows anexemplary preference chart in which a lower number indicates a higherpriority power flow.

TABLE 1 Non- Local Child Parent Critical Critical Energy Micro- Micro-From/To Loads Loads Storage grid(s) grid Local (Renewable) 1 6  6 12 16Energy Sources Local Energy 2 7 N/A 13 16 Storage Child Microgrid(s) 3 810 14 16 Parent Microgrid 4 9 11 15 N/A Local (Non-Renewable) 5 N/A N/AN/A N/A Energy Sources

For example, when using the above priority chart, the microgrid 200, orother local renewable energy source, would first supply power to anyrequesting subsystem that has been identified as a critical load. If thelocal renewable energy source does not have enough power to meet theneeds of the critical load, a local energy storage component or modulemay be used to satisfy the power demands of the critical load. Requestsfor power may then come from a child microgrid, a parent microgrid andfinally a local, non-renewable energy source.

As discussed above, the determination of power requirements may be madein real-time or substantially real-time by the controller 210 in eachmicrogrid 200. In addition, the controller 210 may use received data toforecast data usage of the critical load and/or other subsystems orcomponents. For example, the microgrid 200 may determine that any excessenergy (e.g., energy that is not needed by the critical load) should bestored in local storage rather than being supplied to non-critical loads(as shown in the chart above with each entry having priority 6) as peakenergy usage of a critical load may be upcoming. In anotherimplementation, the microgrid 200 may determine that power should beprovided to non-critical loads prior to storing any excess power.

Although specific examples are discussed and shown in the table above,the priority of such a table may be customizable. In addition, each ofthe categories in the table below may be sub-divided into varioussub-categories. For example, the non-critical load category may furtherbe divided into a “sheddable” category and an “essential” category. Ifthe non-critical load is categorized as sheddable, power may not beprovided to the requesting system when there is a shortage of power. Ifthe non-critical load is categorized as essential, the essential loadstake priority over the sheddable loads. In some implementations, theeach essential load may also be ordered or otherwise prioritized.

Further, although the microgrid 200 may determine whether and when toprovide power to various requesting modules and components, eachmicrogrid may also be customized and/or controlled by an operator. Assuch, an operator may manually set priorities and perform maintenanceactivities on each microgrid.

FIG. 3 illustrates an exemplary method 300 for distributing energy amongcomponents of distributed energy system according to one or moreembodiments of the present disclosure. In some embodiments, the method300 may be used by one or more microgrids to determine how to provide orotherwise allocate power to various components and modules associatedwith or otherwise dependent on the microgrid. In some cases, some of theoperations of the method 300 described below may occur simultaneously orsubstantially simultaneously. For example operation 320 and 330 mayoccur simultaneously or substantially simultaneously and operations 340and 350 may occur simultaneously or substantially simultaneously. Inaddition, operation 310 may be occurring concurrently as previouslyreceived data is being processed by operations 320 through 370 are beingperformed.

Method 300 begins at operation 310 in which data is received fromvarious sources. In some embodiments, the sources may include weatherstations and power stations. In other implementations, the data may bereceived from one or more subsystems and/or microgrids associated withthe microgrid. For example, a microgrid may receive data from one ormore child microgrids or a parent microgrid. In some embodiments, thedata may include historical weather patterns, current weatherinformation, historical power usage, current power usage, current powergeneration, historical power generation and so on.

For example, in some embodiments, the data that is received may indicatepower usage of one or more modules or components that draws power fromthe microgrid at certain times of the day when the temperature outsidereaches various thresholds. In another example, the data may includecurrent or historical usages of power on a weekend.

Once the data is received, flow proceeds to operation 320 and the datais analyzed to determine current and/or forecast energy needs. Morespecifically, the received data may be analyzed to determine currentenergy needs and/or amounts of energy that may be needed in the future.For example, if a microgrid is located on an office building on aweekend in which minimal power is being used, the data may be analyzedto determine that the microgrid typically has to request power fromother microgrids come Monday morning. As a result, the microgrid may optto store the generated power rather than provide it to other requestingmicrogrids or other subsystems.

In addition to the above, the microgrid may analyze the received data todetermine whether it has sufficient power to provide its critical loads.If not, the microgrid may be configured to request power from a parentmicrogrid, a child microgrid, an energy storage module and/or anon-renewable energy source depending on its internal priority tablesuch as described above.

In operation 330, the microgrid provides energy to its critical loads.As discussed above, the microgrid, acting as an autonomous unit, candetermine in real-time or substantially real-time, whether it generatesenough energy to satisfy its critical load requirements. If not, themicrogrid may request power from other sources such as described above.

Flow then proceeds to operation 340 in which the microgrid determines apriority of noncritical load versus storage. As discussed above, usingthe received data, each microgrid may determine that it should storeenergy for upcoming requests rather than provide energy to non-criticalloads. In other scenarios, the microgrid may determine that providingpower to non-critical loads is, at the moment, more important thanstoring the power for subsequent use.

Flow then proceeds to operation 350 and the power is provided to thenon-critical loads and/or a power storage module. As discussed above,the priority between the non-critical load and the storage may vary fromtime to time.

In operation 360, the microgrid may also be configured to determinepower needs and/or requirements of one or more child microgrids and itsparent microgrid. For example, the microgrid may be configured toreceive data and/or power requests from child and parent microgrids.Once the data is received, the microgrid can determine whether and whento provide the requested power to the requesting microgrids.

If the microgrid determines that is has sufficient power or otherwisedetermines it wants to provide power to the requesting microgrids, flowproceeds to operation 370 and the microgrid provides the requested powerto one or more child microgrids and to a parent microgrid. As discussedabove, the priority between child microgrids and parent microgrids maybe established using a priority table such as described above. Themethod 300 may then cycle back to operation 320 and the process may berepeated for another set of data.

FIG. 4 is a block diagram illustrating exemplary components, such as,for example, hardware components of a computing device 400 according toone or more embodiments of the present disclosure. In certainembodiments, the computing device 400 may be part of a distributedenergy system such as, for example, a component of the distributedenergy system. For example, the computing device 400, or variouscomponents or modules of the computing device 400 may be part of orotherwise included with a microgrid. Although various components of thecomputing device 400 are shown, connections and communication channelsbetween each of the components are omitted for simplicity.

In a basic configuration, the computing device 400 may include at leastone controller or processor 405 and an associated memory 410. The memory410 may include, but is not limited to, volatile storage such as randomaccess memory, non-volatile storage such as read-only memory, flashmemory, or any combination thereof. The memory 410 may store anoperating system 415 and one or more program modules 420 suitable forrunning software applications 455. The operating system 415 may beconfigured to control the computing device 400 and/or one or moresoftware applications 455 being executed by the operating system 415.The program modules 420 or software applications 455 may include modulesand programs for requesting data, analyzing received data, determiningthe priority of energy requestors as well as providing communicationsbetween modules and components of the microgrid and/or other componentsand modules of a distributed energy system and so on.

The computing device 400 may have additional features or functionalitythan those expressly described herein. For example, the computing device400 may also include additional data storage devices, removable andnon-removable, such as, for example, magnetic disks, optical disks, ortape. Exemplary storage devices are illustrated in FIG. 4 by removablestorage device 425 and a non-removable storage device 430.

In certain embodiments, various program modules and data files may bestored in the system memory 410. The program modules 420 and theprocessor 405 may perform processes that include one or more of theoperations of method 300 shown and described with respect to FIG. 3.

As also shown in FIG. 4, the computing device 400 may include one ormore input devices 435. The input devices 435 may include a keyboard, amouse, a pen or stylus, a sound input device, a touch input device, andthe like. The computing device 400 may also include one or more outputdevices 440. The output devices 440 may include a display, one or morespeakers, a printer, and the like.

The computing device 400 also includes communication connections 445that facilitate communications with additional computing devices 450.Such communication connections 445 may include internet capabilities, aRF transmitter, a receiver, and/or transceiver circuitry, universalserial bus (USB) communications, parallel ports and/or serial ports.

As used herein, the term computer readable media may include computerstorage media. Computer storage media may include volatile andnonvolatile media and/or removable and non-removable media for thestorage of information. Examples include computer-readable instructions,data structures, and program modules. The memory 410, the removablestorage device 425, and the non-removable storage device 430 are allexamples of computer storage media. Computer storage media may includeRAM, ROM, electrically erasable read-only memory (EEPROM), flash memoryor other memory technology, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other article ofmanufacture which can be used to store information and which can beaccessed by the computing device 400. Any such computer storage mediamay be part of the computing device 400.

Embodiments of the present disclosure are described above with referenceto block diagrams and operational illustrations of methods and the like.The operations described may occur out of the order as shown in any ofthe figures. Additionally, one or more operations may be removed orexecuted substantially concurrently. For example, two blocks shown insuccession may be executed substantially concurrently. Additionally, theblocks may be executed in the reverse order.

The description and illustration of one or more embodiments provided inthis disclosure are not intended to limit or restrict the scope of thepresent disclosure as claimed. The embodiments, examples, and detailsprovided in this disclosure are considered sufficient to conveypossession and enable others to make and use the best mode of theclaimed embodiments. Additionally, the claimed embodiments should not beconstrued as being limited to any embodiment, example, or detailprovided above. Regardless of whether shown and described in combinationor separately, the various features, including structural features andmethodological features, are intended to be selectively included oromitted to produce an embodiment with a particular set of features.Having been provided with the description and illustration of thepresent application, one skilled in the art may envision variations,modifications, and alternate embodiments falling within the spirit ofthe broader aspects of the embodiments described herein that do notdepart from the broader scope of the claimed embodiments.

We claim:
 1. A method for distributing energy, the method comprising:receiving, at a microgrid, data from a plurality of data sources;analyzing the data to forecast power needs associated with themicrogrid; and providing power from the microgrid to one or morerequesting modules based, at least in part, on the forecast.
 2. Themethod of claim 1, wherein providing energy from the microgrid to one ormore requesting modules comprises establishing a priority of therequesting modules.
 3. The method of claim 2, wherein the priority ofthe requesting modules comprise a critical requesting module and anon-critical requesting module.
 4. The method of claim 3, wherein thenon-critical requesting module comprises an energy storage module. 5.The method of claim 1, wherein the one or more requesting modules is aparent microgrid.
 6. The method of claim 1, wherein the one or morerequesting modules is a child microgrid.
 7. The method of claim 1,wherein the data sources comprise one or more components that receivepower from the microgrid.
 8. The method of claim 1, wherein the datasources comprise the one or more requesting modules.
 9. Acomputer-readable storage medium encoding computer executableinstructions which, when executed by a processor, performs a method fordistributing energy, the method comprising: receiving, at a microgrid,data from a plurality of data sources; analyzing the data to forecastpower needs associated with the microgrid; and providing power from themicrogrid to one or more requesting modules based, at least in part, onthe forecast.
 10. The computer-readable storage medium of claim 9,wherein providing energy from the microgrid to one or more requestingmodules comprises establishing a priority of the requesting modules. 11.The computer-readable storage medium of claim 10, wherein the priorityof the requesting modules comprise a critical requesting module and anon-critical requesting module.
 12. The computer-readable storage mediumof claim 11, wherein the non-critical requesting module comprises anenergy storage module.
 13. The computer-readable storage medium of claim9, wherein the one or more requesting modules is a parent microgrid. 14.The computer-readable storage medium of claim 9, wherein the one or morerequesting modules is a child microgrid.
 15. The computer-readablestorage medium of claim 9, wherein the data sources comprise one or morecomponents that receive power from the microgrid.
 16. Thecomputer-readable storage medium of claim 9, wherein the data sourcescomprise the one or more requesting modules.
 17. A system comprising: aprocessor; and a memory coupled to the processor, the memory for storinginstructions which, when executed by the processor, causes the processto perform a method for distributing energy, the method comprising:receiving, at a microgrid, data from a plurality of data sources;analyzing the data to forecast power needs associated with themicrogrid; and providing power from the microgrid to one or morerequesting modules based, at least in part, on the forecast.
 18. Thesystem of claim 17, wherein providing energy from the microgrid to oneor more requesting modules comprises establishing a priority of therequesting modules.
 19. The system of claim 18, wherein the priority ofthe requesting modules comprises a critical requesting module and anon-critical requesting module.
 20. The system of claim 19, wherein thenon-critical requesting module comprises an energy storage module.